Show fewer classes in docs when redundant

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.23.0.10
+Version: 0.23.0.11
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",

NEWS.md

@@ -11,6 +11,9 @@
 
 * Added support for `anova()` from models of the *survey* package.
 
+* Documentation was re-organized and clarified, and the index reduced by removing
+  redundant class-documentation.
+
 ## Bug fixes
 
 * Fixed bug when extracting 'pretty labels' for model parameters, which could

R/1_model_parameters.R

@@ -10,16 +10,17 @@
 #' - [Default method][model_parameters.default()]: `lm`, `glm`, **stats**, **censReg**,
 #'   **MASS**, **survey**, ...
 #' - [Additive models][model_parameters.cgam()]: **bamlss**, **gamlss**, **mgcv**,
-#'   **scam**, **VGAM**, `Gam`, `gamm`, ...
-#' - [ANOVA][model_parameters.aov()]: **afex**, `aov`, `anova`, ...
+#'   **scam**, **VGAM**, `Gam` (although the output of `Gam` is more Anova-alike),
+#'   `gamm`, ...
+#' - [ANOVA][model_parameters.aov()]: **afex**, `aov`, `anova`, `Gam`, ...
 #' - [Bayesian][model_parameters.stanreg()]: **BayesFactor**, **blavaan**, **brms**,
 #'   **MCMCglmm**, **posterior**, **rstanarm**, `bayesQR`, `bcplm`, `BGGM`, `blmrm`,
 #'   `blrm`, `mcmc.list`, `MCMCglmm`, ...
-#' - [Clustering][model_parameters.kmeans()]: **hclust**, **kmeans**, **mclust**, **pam**, ...
+#' - [Clustering][model_parameters.hclust()]: **hclust**, **kmeans**, **mclust**, **pam**, ...
 #' - [Correlations, t-tests, etc.][model_parameters.htest()]: **lmtest**, `htest`,
 #'   `pairwise.htest`, ...
 #' - [Meta-Analysis][model_parameters.rma()]: **metaBMA**, **metafor**, **metaplus**, ...
-#' - [Mixed models][model_parameters.merMod()]: **cplm**, **glmmTMB**, **lme4**,
+#' - [Mixed models][model_parameters.glmmTMB()]: **cplm**, **glmmTMB**, **lme4**,
 #'   **lmerTest**, **nlme**, **ordinal**, **robustlmm**, **spaMM**, `mixed`, `MixMod`, ...
 #' - [Multinomial, ordinal and cumulative link][model_parameters.mlm()]: **brglm2**,
 #'   **DirichletReg**, **nnet**, **ordinal**, `mlm`, ...
@@ -28,13 +29,16 @@
 #'   **psych**, `sem`, ...
 #' - [Zero-inflated and hurdle][model_parameters.zcpglm()]: **cplm**, **mhurdle**,
 #'   **pscl**, ...
-#' - [Other models][model_parameters.averaging()]: **aod**, **bbmle**, **betareg**,
+#' - [Other models][model_parameters.glimML()]: **aod**, **bbmle**, **betareg**,
 #'   **emmeans**, **epiR**, **ggeffects**, **glmx**, **ivfixed**, **ivprobit**,
 #'   **JRM**, **lmodel2**, **logitsf**, **marginaleffects**, **margins**, **maxLik**,
 #'   **mediation**, **mfx**, **multcomp**, **mvord**, **plm**, **PMCMRplus**,
 #'   **quantreg**, **selection**, **systemfit**, **tidymodels**, **varEST**,
 #'   **WRS2**, `bfsl`, `deltaMethod`, `fitdistr`, `mjoint`, `mle`, `model.avg`, ...
 #'
+#' A full overview can be found here:
+#' https://easystats.github.io/parameters/reference/
+#'
 #' @param model Statistical Model.
 #' @param ... Arguments passed to or from other methods. Non-documented
 #' arguments are
@@ -180,7 +184,7 @@
 #'
 #' Compared to fixed effects (or single-level) models, determining appropriate
 #' df for Wald-based inference in mixed models is more difficult.
 #' See [the R GLMM FAQ](https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#what-are-the-p-values-listed-by-summaryglmerfit-etc.-are-they-reliable)
 #' for a discussion.
 #'
 #' Several approximate methods for computing df are available, but you should
@@ -757,7 +761,6 @@
 #################### .glm ----------------------
 
 
-#' @rdname model_parameters.default
 #' @export
 model_parameters.glm <- function(model,
                                  ci = 0.95,

R/2_ci.R

@@ -6,33 +6,35 @@
 #' @param x A statistical model.
 #' @param ci Confidence Interval (CI) level. Default to `0.95` (`95%`).
 #' @param dof Number of degrees of freedom to be used when calculating
-#'   confidence intervals. If `NULL` (default), the degrees of freedom are
-#'   retrieved by calling [`insight::get_df()`] with approximation method
-#'   defined in `method`. If not `NULL`, use this argument to override the
-#'   default degrees of freedom used to compute confidence intervals.
-#' @param method Method for computing degrees of freedom for
-#'   confidence intervals (CI) and the related p-values. Allowed are following
-#'   options (which vary depending on the model class): `"residual"`,
-#'   `"normal"`, `"likelihood"`, `"satterthwaite"`, `"kenward"`, `"wald"`,
-#'   `"profile"`, `"boot"`, `"uniroot"`, `"ml1"`, `"betwithin"`, `"hdi"`,
-#'   `"quantile"`, `"ci"`, `"eti"`, `"si"`, `"bci"`, or `"bcai"`. See section
-#'   _Confidence intervals and approximation of degrees of freedom_ in
-#'   [`model_parameters()`] for further details.
+#' confidence intervals. If `NULL` (default), the degrees of freedom are
+#' retrieved by calling [`insight::get_df()`] with approximation method defined
+#' in `method`. If not `NULL`, use this argument to override the default degrees
+#' of freedom used to compute confidence intervals.
+#' @param method Method for computing degrees of freedom for confidence
+#' intervals (CI) and the related p-values. Allowed are following options (which
+#' vary depending on the model class): `"residual"`, `"normal"`, `"likelihood"`,
+#' `"satterthwaite"`, `"kenward"`, `"wald"`, `"profile"`, `"boot"`, `"uniroot"`,
+#' `"ml1"`, `"betwithin"`, `"hdi"`, `"quantile"`, `"ci"`, `"eti"`, `"si"`,
+#' `"bci"`, or `"bcai"`. See section _Confidence intervals and approximation of
+#' degrees of freedom_ in [`model_parameters()`] for further details.
 #' @param component Model component for which parameters should be shown. See
-#'   the documentation for your object's class in [`model_parameters()`] or
-#'   [`p_value()`] for further details.
+#' the documentation for your object's class in [`model_parameters()`] or
+#' [`p_value()`] for further details, or see section _Model components_.
 #' @param iterations The number of bootstrap replicates. Only applies to models
-#'   of class `merMod` when `method=boot`.
+#' of class `merMod` when `method=boot`.
 #' @param verbose Toggle warnings and messages.
 #' @param ... Additional arguments passed down to the underlying functions.
 #' E.g., arguments like `vcov` or `vcov_args` can be used to compute confidence
 #' intervals using a specific variance-covariance matrix for the standard
 #' errors.
+#' @inheritParams standard_error
 #'
 #' @return A data frame containing the CI bounds.
 #'
 #' @inheritSection model_parameters Confidence intervals and approximation of degrees of freedom
 #'
+#' @inheritSection model_parameters.zcpglm Model components
+#'
 #' @examplesIf require("glmmTMB") && requireNamespace("sandwich")
 #' data(qol_cancer)
 #' model <- lm(QoL ~ time + age + education, data = qol_cancer)
@@ -57,10 +59,29 @@
 #' ci(model, component = "zi")
 #' }
 #' @export
-ci.default <- function(x, ci = 0.95, dof = NULL, method = NULL, ...) {
+ci.default <- function(x,
+                       ci = 0.95,
+                       dof = NULL,
+                       method = NULL,
+                       iterations = 500,
+                       component = "all",
+                       vcov = NULL,
+                       vcov_args = NULL,
+                       verbose = TRUE,
+                       ...) {
   # check for valid input
   .is_model_valid(x)
-  .ci_generic(model = x, ci = ci, dof = dof, method = method, ...)
+  .ci_generic(
+    model = x,
+    ci = ci,
+    dof = dof,
+    method = method,
+    component = component,
+    vcov = vcov,
+    vcov_args = vcov_args,
+    verbose = verbose,
+    ...
+  )
 }
 
 
@@ -73,7 +94,10 @@ ci.glm <- function(x,
                    vcov_args = NULL,
                    verbose = TRUE,
                    ...) {
-  method <- insight::validate_argument(method, c("profile", "wald", "normal", "residual"))
+  method <- insight::validate_argument(
+    method,
+    c("profile", "wald", "normal", "residual")
+  )
 
   # No robust vcov for profile method
   if (method == "profile") {

R/3_p_value.R

@@ -2,11 +2,7 @@
 #' @name p_value
 #'
 #' @description This function attempts to return, or compute, p-values of a model's
-#' parameters. See the documentation for your object's class:
-#' - [Bayesian models][p_value.BFBayesFactor] (**rstanarm**, **brms**, **MCMCglmm**, ...)
-#' - [Zero-inflated models][p_value.zeroinfl] (`hurdle`, `zeroinfl`, `zerocount`, ...)
-#' - [Marginal effects models][p_value.poissonmfx] (**mfx**)
-#' - [Models with special components][p_value.DirichletRegModel] (`DirichletRegModel`, `clm2`, `cgam`, ...)
+#' parameters.
 #'
 #' @param model A statistical model.
 #' @param adjust Character value naming the method used to adjust p-values or
@@ -17,14 +13,29 @@
 #'
 #' @inheritSection model_parameters Confidence intervals and approximation of degrees of freedom
 #'
+#' @inheritSection model_parameters.zcpglm Model components
+#'
+#' @details
+#' For Bayesian models, the p-values corresponds to the *probability of
+#' direction* ([`bayestestR::p_direction()`]), which is converted to a p-value
+#' using `bayestestR::convert_pd_to_p()`.
+#'
 #' @return A data frame with at least two columns: the parameter names and the
 #'   p-values. Depending on the model, may also include columns for model
 #'   components etc.
 #'
-#' @examples
+#' @examplesIf require("pscl", quietly = TRUE)
 #' data(iris)
 #' model <- lm(Petal.Length ~ Sepal.Length + Species, data = iris)
 #' p_value(model)
+#'
+#' data("bioChemists", package = "pscl")
+#' model <- pscl::zeroinfl(
+#'   art ~ fem + mar + kid5 | kid5 + phd,
+#'   data = bioChemists
+#' )
+#' p_value(model)
+#' p_value(model, component = "zi")
 #' @export
 p_value <- function(model, ...) {
   UseMethod("p_value")

R/4_standard_error.R

@@ -1,6 +1,7 @@
-#' Standard Errors
+#' @title Standard Errors
+#' @name standard_error
 #'
-#' `standard_error()` attempts to return standard errors of model
+#' @description `standard_error()` attempts to return standard errors of model
 #' parameters.
 #'
 #' @param model A model.
@@ -21,8 +22,8 @@
 #'    - Cluster-robust: `"CR"`, `"CR0"`, `"CR1"`, `"CR1p"`, `"CR1S"`,
 #'      `"CR2"`, `"CR3"`. See `?clubSandwich::vcovCR`
 #'    - Bootstrap: `"BS"`, `"xy"`, `"residual"`, `"wild"`, `"mammen"`,
-#'      `"fractional"`, `"jackknife"`, `"norm"`, `"webb"`.
-#'      See `?sandwich::vcovBS`
+#'      `"fractional"`, `"jackknife"`, `"norm"`, `"webb"`. See
+#'      `?sandwich::vcovBS`
 #'    - Other `sandwich` package functions: `"HAC"`, `"PC"`, `"CL"`, `"OPG"`,
 #'      `"PL"`.
 #' @param vcov_args List of arguments to be passed to the function identified by
@@ -75,6 +76,7 @@ standard_error <- function(model, ...) {
 #' @rdname standard_error
 #' @export
 standard_error.default <- function(model,
+                                   effects = "fixed",
                                    component = "all",
                                    vcov = NULL,
                                    vcov_args = NULL,

R/5_simulate_model.R

@@ -15,6 +15,8 @@
 #' @inheritParams bootstrap_model
 #' @inheritParams p_value
 #'
+#' @inheritSection model_parameters.zcpglm Model components
+#'
 #' @return A data frame.
 #'
 #' @seealso [`simulate_parameters()`], [`bootstrap_model()`], [`bootstrap_parameters()`]
@@ -57,9 +59,9 @@
 
 # Models with single component only -----------------------------------------
 
-
+#' @rdname simulate_model
 #' @export
-simulate_model.default <- function(model, iterations = 1000, ...) {
+simulate_model.default <- function(model, iterations = 1000, component = "all", ...) {
   # check for valid input
   .is_model_valid(model)
 
@@ -229,7 +231,11 @@
 # helper -----------------------------------------
 
 
-.simulate_model <- function(model, iterations, component = "conditional", effects = "fixed", ...) {
+.simulate_model <- function(model,
+                            iterations,
+                            component = "conditional",
+                            effects = "fixed",
+                            ...) {
   if (is.null(iterations)) iterations <- 1000
 
   params <- insight::get_parameters(model, effects = effects, component = component, verbose = FALSE)
@@ -252,7 +258,7 @@
   # }
 }
 
 .mvrnorm <- function(n = 1, mu, Sigma, tol = 1e-06) {
   p <- length(mu)
   if (!all(dim(Sigma) == c(p, p))) {
     insight::format_error(

R/bootstrap_model.R

@@ -66,17 +66,21 @@ bootstrap_model <- function(model,
 bootstrap_model.default <- function(model,
                                     iterations = 1000,
                                     type = "ordinary",
-                                    parallel = c("no", "multicore", "snow"),
+                                    parallel = "no",
                                     n_cpus = 1,
+                                    cluster = NULL,
                                     verbose = FALSE,
                                     ...) {
   # check for valid input
   .is_model_valid(model)
 
   insight::check_if_installed("boot")
 
-  type <- insight::validate_argument(type, c("ordinary", "parametric", "balanced", "permutation", "antithetic"))
-  parallel <- match.arg(parallel)
+  type <- insight::validate_argument(
+    type,
+    c("ordinary", "parametric", "balanced", "permutation", "antithetic")
+  )
+  parallel <- insight::validate_argument(parallel, c("no", "multicore", "snow"))
 
   model_data <- data <- insight::get_data(model, verbose = FALSE) # nolint
   model_response <- insight::find_response(model)
@@ -144,20 +148,19 @@ bootstrap_model.default <- function(model,
 }
 
 
-#' @rdname bootstrap_model
 #' @export
 bootstrap_model.merMod <- function(model,
                                    iterations = 1000,
                                    type = "parametric",
-                                   parallel = c("no", "multicore", "snow"),
+                                   parallel = "no",
                                    n_cpus = 1,
                                    cluster = NULL,
                                    verbose = FALSE,
                                    ...) {
   insight::check_if_installed("lme4")
 
   type <- insight::validate_argument(type, c("parametric", "semiparametric"))
-  parallel <- match.arg(parallel)
+  parallel <- insight::validate_argument(parallel, c("no", "multicore", "snow"))
 
   boot_function <- function(model) {
     params <- insight::get_parameters(model, verbose = FALSE)
@@ -222,14 +225,17 @@ bootstrap_model.glmmTMB <- bootstrap_model.merMod
 bootstrap_model.nestedLogit <- function(model,
                                         iterations = 1000,
                                         type = "ordinary",
-                                        parallel = c("no", "multicore", "snow"),
+                                        parallel = "no",
                                         n_cpus = 1,
                                         verbose = FALSE,
                                         ...) {
   insight::check_if_installed("boot")
 
-  type <- insight::validate_argument(type, c("ordinary", "balanced", "permutation", "antithetic"))
-  parallel <- match.arg(parallel)
+  type <- insight::validate_argument(
+    type,
+    c("ordinary", "balanced", "permutation", "antithetic")
+  )
+  parallel <- insight::validate_argument(parallel, c("no", "multicore", "snow"))
 
   model_data <- data <- insight::get_data(model, verbose = FALSE) # nolint
   model_response <- insight::find_response(model)